A decentralized, public auditable FLchain system is designed in [1]} with trust and incentive to accelerate the cooperation in FL training without worrying about trust among entities. In fact, blockchain is promising to store securely information of trainers and FL models during the FL process, and mining helps to verify incorrect updates from FL computation. Particularly, any clients who detect any misbehaviour are rewarded and affected trainers are compensated through the blockchain-based incentive mechanism. Similarly, a value-driven incentive mechanism with blockchain is designed for FL, called DeepChain, aiming to encourage parties to behave correctly and share actively the obtained local gradients during the collaborative learning [2]}. Each party can get rewards or penalties based on his contribution. The benefits of learning incentivization are twofold, including attracting more clients to join the training and ensuring that clients are honest in local model training and update exchange. Also, model updates are verified and audited by blockchain, while gradient collecting and parameter update are monitored by all participants over the ledger network, which thus ensures fairness in the FL process. In addition to incentivization, FL servers need to pay clients as a contribution cost after the FL training. To solve this issue, a payment method is considered in [3]} by designing a Class-Sampled Validation-Error scheme to validate the gradients of clients to specify reasonable device rewards. A smart contract is integrated in a Hyperledger Fabric blockchain to implement gradient verification and reward scheduling in a transparent and trustful way [4]}. In this way, clients can benefit by retaining ownership and receiving incentives for their data, while model owners are able to access a larger dataset, which thus enhance the robustness of the overall FL learning.
